Smartwatch blackbox

ABSTRACT

Techniques are provided for alerting drivers of hazardous driving conditions using the sensing capabilities of wearable mobile technology. In one aspect, a method for alerting drivers of hazardous driving conditions includes the steps of: collecting real-time data from a driver of a vehicle, wherein the data is collected via a mobile device worn by the driver; determining whether the real-time data indicates that a hazardous driving condition exists; providing feedback to the driver if the real-time data indicates that a hazardous driving condition exists, and continuing to collect data from the driver in real-time if the real-time data indicates that a hazardous driving condition does not exist. The real-time data may also be collected and used to learn characteristics of the driver. These characteristics can be compared with the data being collected to help determine, in real-time, whether the driving behavior is normal and whether a hazardous driving condition exists.

FIELD OF THE INVENTION

The present invention relates to safe driving practices, and moreparticularly, to techniques for alerting drivers of hazardous drivingconditions using the sensing capabilities of wearable mobile technology,such as a smartwatch, to provide real-time feedback to drivers based ondata collected via the smartwatch.

BACKGROUND OF THE INVENTION

People want to avoid automobile accidents, including those caused bytheir own behavior and emotions which can adversely affect their drivingsuccess. Insurance companies would like to get a better risk profile ofoperators.

Current technology for tracking driving performance is generally basedon data collected from a vehicle. See, for example, U.S. Pat. No.8,090,598 issued to Bauer et al., entitled “Monitoring System forDetermining and Communicating a Cost of Insurance,” wherein driving datais collected via a vehicle's on board data (OBD) collector. Thistechnology is, however, tied to the vehicle itself rather than to thedriver. Thus, data collected relates only generally to whoever isdriving the vehicle.

Further, since information is collected via the vehicle itself, the datarelates only to movement and operation of the vehicle. While it isassumed that the operation of the vehicle is a direct result of actionsby the driver, there are many factors related to driving safety thatcannot be accounted for. For instance, distractions, stress, etc. mayimpact a driver in a manner not detectable by the vehicle-based system.It would be desirable to be able to detect these situations and warndrivers of any potential hazards.

Therefore, techniques for monitoring drivers themselves and for warningthe drivers when potentially hazardous situations arise would bedesirable.

SUMMARY OF THE INVENTION

The present invention provides techniques for alerting drivers ofhazardous driving conditions using the sensing capabilities of wearablemobile technology, such as a smartwatch, to provide real-time feedbackto drivers based on data collected via the smartwatch. In one aspect ofthe invention, a method for alerting drivers of hazardous drivingconditions is provided. The method includes the steps of: collectingreal-time data from a driver of a vehicle, wherein the data is collectedvia a mobile device worn by the driver; determining whether thereal-time data indicates that a hazardous driving condition exists;providing feedback to the driver if the real-time data indicates that ahazardous driving condition exists; and continuing to collect data fromthe driver in real-time if the real-time data indicates that a hazardousdriving does not exist. The real-time data may also be collected andused to learn characteristics of the driver. These characteristics canbe compared with the data being collected to help determine, inreal-time, whether the driving behavior is normal and whether ahazardous driving condition exists.

A more complete understanding of the present invention, as well asfurther features and advantages of the present invention, will beobtained by reference to the following detailed description anddrawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a diagram illustrating an exemplary methodology for alertingdrivers of potentially hazardous driving conditions according to anembodiment of the present invention;

FIG. 2 is a diagram illustrating an exemplary implementation of thepresent techniques in a scenario where the potentially hazardous drivingconditions include weaving according to an embodiment of the presentinvention;

FIG. 3 is a diagram illustrating an exemplary implementation of thepresent techniques in a scenario where the potentially hazardous drivingconditions include distracted driving according to an embodiment of thepresent invention;

FIG. 4 is a diagram illustrating an exemplary implementation of thepresent techniques in a scenario where the potentially hazardous drivingconditions include tailgating according to an embodiment of the presentinvention;

FIG. 5 is a diagram illustrating an exemplary system for alertingdrivers of potentially hazardous driving conditions according to anembodiment of the present invention; and

FIG. 6 is a diagram illustrating an exemplary apparatus for performingone or more of the methodologies presented herein according to anembodiment of the present invention.

DETAILED DESCRIPTION OF PREFERRED EMBODIMENTS

As provided above, there is a need for driver-based technology tomonitor and warn drivers of potentially hazardous situations. Thistechnology would collect data from the driver him/herself, rather thanfrom the vehicle he/she is driving. This would provide a wealth ofinformation not available from a vehicle-tied system. For instance, aswill be described in detail below, there are indicators of stress,fatigue, impairment, etc. that can be gleaned from data collected from adriver, that might not be apparent at the vehicle operation level.

Advantageously, the present techniques leverage the capabilities ofemerging smartwatch technology to capture data from a driver andimplement safe driving practices. As will be described in detail below,smartwatches include a variety of sensors capable of collecting a vastamount of data from the user. Further, since the smartwatch is awearable technology, users will often be wearing the smartwatch on adaily basis, such as when driving. By comparison, a smartphone, forexample, is often carried as an accessory and is often not on the user'sperson when he/she is driving. Also, a smartwatch is typically worn onthe user's wrist. Thus, useful data related to the user's arm movementscan be collected via the smartwatch while the user is driving.

According to an exemplary embodiment, the present smartwatch-basedtechniques are used to collect (in real-time) driving data from a userand provide real-time warnings to the user of potentially hazardoussituations. For instance, erratic side-to-side arm movements, highdriving speeds (e.g., detected via the smartwatch global positioningsystem (GPS)—see below), etc. can be indicators of aggressive driving,and the present techniques may be used to alert the driver (e.g., viathe driver's smartwatch and/or other available communication technology)to “slow down,” “relax,” etc.

As provided above, insurance companies might also have an interest inmonitoring driver performance. Thus according to an exemplaryembodiment, the smartwatch used in accordance with the presenttechniques can be provided by the insurance company to its insureddrivers. For example, the insurance company might provide the smartwatchto its customers at a low-cost or even free, with the incentive for itsuse being a lower insurance rate for safer drivers. The insurancecompany can collect data from the driver when he/she is driving. Thesmartwatch in this case may be a “blackbox” to the user, in that theuser might not care what software is present on the (insurancecompany-supplied) smartwatch and/or what specific driving data is beingcollected as long as they are rewarded with a lower insurance rate forsafer driving and/or are alerted (as described above) when hazardousconditions arise.

While the example of a smartwatch is used in the instant description,the present techniques are broadly applicable to any wearable technology(e.g., smartwatch, smartglasses, etc.) capable of directly acquiringreal-time data from a user, and thus may be referred to generally hereinas a wearable mobile device. Smartwatches which may be used inaccordance with the present techniques are available from companies suchas Motorola™ (e.g., the MOTO 360), Samsung™ (e.g., Samsung Gear™),Apple™ (e.g., the Apple Watch™), etc.

A non-exhaustive list of smartwatch capabilities that may be leveragedin accordance with the present techniques is now provided. Differentsmartwatches (or other suitable wearable technology) have differentcapabilities, such as a variety of different sensors, user interactivefeatures such as voice commands, audible/motion alarms/alerts, etc. Byway of example only, some of the smartwatch technology that may beleveraged for the present techniques includes the following:

Sensors—the present techniques envision use of one or more sensorsproximate to the user (also referred to herein as proximal sensors).These are sensors that can measure physical/physiological conditions ofthe user. These types of sensors generally require contact with the userto function, and thus are also referred to herein as contact sensors.For instance, one such contact sensor is an electrodermal activity orEDA sensor. EDA sensors measure the electrical characteristics of theskin. The electrical characteristics of the skin are controlled, atleast in part, by the state of sweat glands in the skin, which in turnare regulated by the sympathetic nervous system. Thus, EDA sensors cangauge sympathetic and nervous responses.

More specifically, based on a sweat gland circuit-loop, EDA measuresstrength of change in skin conductance to electrical charge asreflecting sympathetic nervous system response to sensation. This changeis associated with eccrine sweat-gland activity innervated by thesympathetic branch of the autonomic nervous system. Reactions cannot becontrolled instantly with the mind, thus measurements reliably recordstress caused by external stimuli.

EDA data is classified as either tonic—low amplitude, low frequencywaveforms typical in a relaxed state—or phasic—higher amplitude, higherfrequency waveforms occurring 1 to 3 seconds after a sensory stimulus.This phasic measurement is the skin conductance response (SCR). Sensorystimulus can be auditory, visual, olfactory, tactile, or vestibular(vertigo, imbalance).

In the context of the present techniques, EDA sensors can be used tocollect real-time data indicating a level of stress of the driver, e.g.,the driver is in a relaxed or high-anxiety/stress state. For instance,based on the above-described sensory stimulus, the driver might be understress when he/she is driving aggressively, is in an accident, witnessesan accident or other hazardous road condition (e.g., other motoristsaround them driving improperly or speeding, slick road conditions,debris on the road, etc.)—based on the above-described sensory stimulus.

Other contact sensors useful for the present techniques include pulseoximeters and heart rate sensors. A pulse oximeter measures a person'sblood oxygen levels often via a sensor placed on a part of the body suchas a fingertip. Similarly, a heart rate sensor measures a person's heartrate or pulse (generally in beats per minute), e.g., via a sensor placedon the chest or wrist.

With regard to the present techniques, the driver's pulse/heart rate maybe indicators of stress. As described above, driver stress may be causedby their driving condition and/or that of others, road conditions andhazards, etc. These vitals are also indicators of impairment conditions,such as when the driver is experiencing a medical condition like a heartcondition. It is assumed that under normal driving conditions, a userwill have a constant pulse/heart rate. Thus, drastic changes (spike ordip) in driver pulse/heart rate can be indicators of hazardous drivingconditions.

Other useful proximal sensors are trajectory and pose sensors. Forinstance, an accelerometer can be used to detect the user's movement,speed and direction. A gyroscope sensor (often used in conjunction withan accelerometer) detects direction or orientation. A rate gyroscopesimilarly measures the rate of change of angle with time. A globalpositioning system or GPS provides location information.

An accelerometer can provide useful data driving performance. Forexample, abrupt accelerations and/or abrupt decelerations can indicatethat the driver is speeding up very quickly and/or that the driver isslamming on the brakes. Both of these situations can be indicators of ahazardous condition. For instance, a driver that repeatedly slams on thebrakes (detected via rapid deceleration) might be following too close tothe driver in front of them and/or may be travelling at too high a rateof speed for the driving conditions (such as speeding in stop and gotraffic).

With regard to the user's movements (detected, e.g., via thegyroscope/accelerometer), as provided above, data related to the user'sarm motion while driving can be useful. For instance, rapid side-to-sidearm motions might indicate weaving and/or other types of aggressivedriving that might indicate a hazardous situation. For example, a driverthat is weaving in and out of traffic is more likely to be involved in(or cause) an accident. Thus, when this type of driving behavior isdetected, the driver might be prompted with an audible alert message (onhis/her smartwatch and/or via other non-distracting means (such an audiowarning through the vehicle's radio) to “slow down” or “maintain safedriving practices.” Uncharacteristic arm movements might also indicatethat the driver is doing something other than focusing on driving. Forinstance, the driver might be talking to a passenger and gesturing withhis/her arm rather than gripping the steering wheel, the driver might bereaching for items in the vehicle, etc. All of these activities can leadto unsafe driving conditions.

A gyroscope sensor can provide data useful for detecting unusual drivingoccurrences. For instance, a sudden veering to the left or right mightindicate that the driver has accidentally moved out of his/her lane oroff of the road, is making drastic lane changes, is experiencing slickroad conditions, etc.

GPS sensors provide a convenient way to determine the speed at which thedriver (and thus the vehicle) is travelling. Speedometer capabilitiesare present in many current GPS modules. Thus, if the present systemdetects that the driver is speeding (e.g., based on current location andposted speed limits, size/type of road, weather/environmentalconditions, etc. for that location), then an alert may be provided tothe user in the above-described manner to, e.g., “slow down.”

Yet another type of sensor that is useful for the present techniques isan environmental sensor. For instance, a compass and/or a magnetometer(which measures the direction of magnetic fields) can be used todetermine the physical position of the user. A barometer, airtemperature sensors, wind speed sensors, humidity sensors, etc. can beused to assess environmental conditions such as air pressure,temperature, wind velocity etc.

For instance, environmental sensors can be used to assess the weatherconditions which can directly impact the driving conditions. Lowtemperatures in combination with high humidity can be indicators of(ice, snow, etc.) slick road conditions. While the sensors on the user'swearable mobile device might be measuring the environment within thevehicle, it is possible to tie into the vehicle's sensor system, whichoftentimes includes a sensor for temperatures outside of the vehicle.

Useful data can also be collected via the wearable mobile device'smicrophone. The noise level in the vehicle can be an indicator ofdistractions to the driver. For instance, if passengers in the vehicleare talking loudly, children are screaming, the radio is at a highvolume, etc. these all may serve as distractions to a driver. By way ofexample only, noise levels above a predetermined threshold (e.g., set atnormal conversation levels) might be deemed a distraction.

An overview of the present techniques is now provided by way ofreference to methodology 100 of FIG. 1. In step 102, real-time data isgathered from the driver and/or from the driver's surroundings.According to an exemplary embodiment, the real-time data is collectedfrom the driver via the driver's smartwatch. Examples of data that maybe collected using a smartwatch in accordance with the presenttechniques were provided above.

Standard data fusion techniques may be employed to analyze the datacollected from different sensors together. For instance, a driver's(e.g., side-to-side) hand movements (detected via accelerometer) incombination with speed (detected via GPS) may be used to determinewhether the driver is weaving, a combination of vibrations (detected viaaccelerometer) and audio (detected via the microphone) may be used todetermine distracted driving (such as when the driver is distracted bymusic within the vehicle and veers off the road onto warning strips onthe shoulder), repeated stopping and acceleration (detected viaaccelerometer) in combination with road type (detected via GPS location)and/or driver physiological conditions such as heart rate and pulse(detected via heart rate and EDA sensors) can be used to determinetailgating. See examples below.

The real-time data will be used to assess the driver's actions and todetermine whether dangerous conditions exist, issue an alert to thedriver, etc. See below. The data may be analyzed with respect topredetermined threshold values, e.g., more than a predetermined numberof abrupt stops, rapid acceleration, occurrences of speeding,occurrences of veering, etc. It may also be advantageous to take intoaccount the characteristics of the driver. For instance, each persondrives differently, and what might be considered unusual behavior forone driver might be normal for another.

Thus, optionally, in step 104 the real-time data (collected in step 102)is used to learn the driver's characteristics. For instance, the datamay be used to determine what the driver's normal habits are and todetect any activity that falls outside of that norm. To use a simpleexample, suppose a driver A routinely (e.g., more than 90% of the time)travels on the highway at a speed of from 50-55 miles per hour. If thedriver is on the highway and going at a speed much less or greatly inexcess of that range, then it might indicate a hazardous condition.Driver B, on the other hand, routinely (e.g., more than 90% of the time)travels on the highway at a speed of from 45-50 miles per hour. Thus, adifferent standard can be used for the drivers to determine theirdriving conditions. If a common standard was used (e.g., drivers in theUnited States tend to travel between 45-53 miles per hour on highways)then the opportunity to generate an alert might be missed (for instance,based on his/her history driver might be speeding at 55 miles per hour,but not so based on the national average) or unnecessarily (forinstance, based on his/her history it is not unusual for driver A to begoing at 55 miles per hour, however that is above the national average).

A determination is then made as to whether the real-time data indicatesa hazardous driving condition. See step 106. As described above, thiscan involve excessive speed, veering/weaving, sudden braking andacceleration, excessive noise, inclement weather conditions, etc. Asprovided above, this determination can be based on a predeterminedthreshold, e.g., driving above a certain speed, repeated veering orweaving more than a certain number of times, etc. Of course, thesestandards can vary depending on the situation. For instance, as providedabove, speeds acceptable on one road may be excessive on another. Thedata collected might be analyzed based on a set standard (such asnational average of drivers—see above) or, preferably, based oncharacteristics learned from historical data collected from the driver(i.e., data collected over a given period of time—e.g., a week or longersuch as monthly or even yearly).

If it is determined in step 106 that (YES) the driver is exhibitingnormal driving practices (e.g., reasonable speed, no rapidaccelerations, sudden decelerations, etc.), then as shown in FIG. 1, theprocess continues to collect data from the driver in real-time tomonitor driving conditions. As provided above, that data can also becollected/analyzed to establish the driver's characteristics.

On the other hand, if it is determined in step 106 that (NO) the driveris exhibiting unusual driving behavior (with respect to a generalstandard or, preferably, based on the driver's learnedcharacteristics—see above), then in step 108 feedback is provided to thedriver. According to an exemplary embodiment, the feedback is providedto the driver via a message sent through the driver's wearable mobiledevice (e.g., smartwatch) and/or through another communication means,such as through the vehicle's radio. For example, many vehicles todayare Bluetooth enabled, and communications via a user's mobile device canbe routed through the vehicle's radio and/or other hands-free system.

Further, in order to provide useful feedback to the driver, the messagepreferably contains information about the potentially hazardous drivingconditions. To use a simple example, if the real-time data suggests thatthe driver is speeding, then the message might tell the driver to “slowdown.” If the data suggests that the driver is weaving, the messagemight tell the driver to “pay attention to the road” and/or that “roadconditions may be slippery.” The message is in an audible form so thatthe driver doesn't have to take his/her eyes off of the road. Thefeedback message provided to the driver can be selected based on avariety of predetermined scenarios, e.g., via a look-up table format.For example, if the potentially hazardous conditions involve excessspeeds, then the message might be to “slow down,” if the potentiallyhazardous conditions involve aggressive driving (e.g., rapidacceleration, abrupt stops, sudden veering, etc.), then the messagemight be to “calm down,” etc. It is anticipated that multiple feedbackmessages might be provided at the same time if multiple differentpotentially hazardous conditions, e.g., “slow down” and then “slipperyroad conditions possible.” While messages containing specific feedbackfor the conditions has advantages, the scenario is also anticipatedherein where a more general message is provided to the driver, such as“hazardous conditions possible.”

As shown in FIG. 1, the process is performed in an iterative mannerwhile data is collected from the driver in real-time. Thus, if theunusual driving behavior continues and/or other hazards arise, this willbe picked up in the data and subsequent feedback can be provided to thedriver.

Optionally, in step 110, logs may be kept of the potentially hazardousconditions/feedback provided. This information can be reviewed by thedriver to better assess his/her driving and/or to alert others as tohis/her driving history. For instance, parents might want to know howtheir children are driving when they are not with them. Also, asprovided above, insurance companies might track customer's drivinghistories with an incentive being that the customer's insurance rateswill go down if they meet a certain driving safety standard.

The present techniques are further illustrated by the followingnon-limiting exemplary embodiments. In a first exemplary embodiment, thepresent techniques are implemented in the case where the driver isweaving and a determination is made based on fusion of the data from thesensors in the driver's smartwatch. See FIG. 2. Weaving may occur, forexample, when a driver is in a hurry and is moving (left and right) toget around other vehicles on the road.

As provided above, the real-time data is collected from the driver'swearable mobile device (e.g., smartwatch) and analyzed in acontinuous/iterative manner. In the present example, the real-time dataread in step 202 from the accelerometer sensor indicates in step 204that the driver is making lateral motions with his/her hands (handdeviations). The next determination to be made is whether or not thoselateral motions are indicative of weaving, or just normal drivingbehavior. Namely, steering the vehicle generally involves lateralmotions of the driver's hands to move the steering wheel. However, undernormal driving conditions, the lateral side to side motions of thedriver's hands are not generally rapid. Although, when weaving thedriving will tend to turn the steering wheel one way, then rapidly backthe other way, and so on.

Thus, in step 206, a determination is made as to whether the drivingbehavior involves rapid side to side movement of the driver's hand. Touse a simple, non-limiting example, if the driver moves his/her hand toone side and then back the other way in less than or equal to 10seconds, then that might indicate weaving. If it is determined in step206 that (NO) the driver is not making rapid side-to-side handmovements, then the process continues to monitor the driver data inreal-time (see above). On the other hand, if it is determined in step206 that (YES), the drivers hand movements indicate a possibility ofweaving, then the real-time GPS sensor data is used in step 208 toestimate the driver's speed.

If it is determined in step 210 that (NO) the driver is not speeding,then it may be assumed that the side-to-side hand movements have a causeother than weaving (for example, it might be a winding road, there mightbe construction obstacles, etc.), and the process continues to monitorthe driver data in real-time (see above). It is notable that this ismerely an example, and it is possible (via the present techniques) toalert the driver when rapid side-to-side hand movements occur at anyspeed as that alone may be indicative of weaving or other hazardouscondition.

If it is determined in step 210 that (YES) the driver is speeding, thenit may be inferred that the driver is in a hurry to get around othervehicles on the road and weaving, and in step 212 a suggested action(feedback) is provided to the driver, such as “relax” or “slow down.” Asprovided above, this feedback can be provided in a non-distractingmanner via an audio message from the driver's smartwatch and/or throughthe vehicle's audio system.

In a second exemplary embodiment, the present techniques are implementedin the case where the driver is distracted and a determination is madebased on fusion of the data from the sensors in the driver's smartwatch.See FIG. 3. As provided above, distractions can come from noise withinthe vehicle. In this example, the distraction is from music playing onthe vehicle's radio which causes the driver to veer onto the shoulder ofthe road. Oftentimes, roadways have warning strips (called “rumble”strips) on the shoulder that, when driven on, cause vibrationsthroughout the vehicle that can be heard/felt by the driver. This alertsthe driver that he/she has crossed to the shoulder and should move backonto the road.

As provided above, the real-time data is collected from the driver'swearable mobile device (e.g., smartwatch) and analyzed in acontinuous/iterative manner. In the present example, the real-time dataread in step 302 from the accelerometer sensor indicates in step 304that there are vibrations within the vehicle. The next determination tobe made is whether or not those vibrations are indicative of driving onwarning/rumble strips, or just normal road vibrations. For instance,vibrations can occur on any roadway under normal driving conditions.However, the vibrations from normal driving are typically not strong andprolonged. When driving on warning/rumble strips, however, the stripsare evenly spaced and generate intense vibrations throughout thevehicle.

Thus, in step 306, a determination is made as to whether the vibrationsare strong and prolonged, i.e., are above a given frequency andduration. To use a simple, non-limiting example, if constant vibrationsare detected at or above about 10 hertz (Hz) for a duration of greaterthan or equal to about 4 seconds, then that might indicate driving onwarning/rumble strips. If it is determined in step 306 that (NO) thevibrations are not strong/prolonged, then the process continues tomonitor the driver data in real-time (see above). On the other hand, ifit is determined in step 306 that (YES), the vibrations are strong andprolonged and thus could be indicative of the vehicle driving onwarning/rumble strips, then the real-time audio data from the driver'swearable mobile device (e.g., smartwatch) is obtained in step 308 andused in step 310 to classify the type of ambient sounds in the vehicle.Namely, since the scenario being examined here is avoiding distracteddriving, then one may want to determine whether the driving conditions(in this case strong and prolonged vibrations presumed to be fromdriving on warning/rumble strips on the side of the road) are in factcaused by a distracted driver. It is notable however that alerts can begenerated (via the present techniques) whenever strong and prolongedvibrations are detected regardless of any signs of distraction.

For example, high ambient noise (e.g., ambient noise in the vehicle isabove a certain given decibel level) in the vehicle can be classified asloud music playing through the vehicle's audio system. Based on thisclassification, in step 312 a determination can be made as to whetherthe ambient noise in the vehicle is loud music. If it is determined instep 312 that (NO) the ambient noise in the vehicle is not loud (anddistracting) music, then it may be assumed for example that the driverhas purposely driven on the side of the road such as when avoiding anobstacle or due to road construction, etc. The process then continues tomonitor the driver data in real-time (see above).

On the other hand, if it is determined in step 312 that (YES) there isloud music playing in the vehicle, then it may be inferred that thedriver is distracted and as a result has veered off of the road, and instep 314 a suggested action (feedback) is provided to the driver torefocus the driver on the task of driving, such as “pay attention”and/or “turn down the music.” As provided above, this feedback can beprovided in a non-distracting manner via an audio message from thedriver's smartwatch and/or through the vehicle's audio system.

In a third exemplary embodiment, the present techniques are implementedin the case where the driver is tailgating and a determination is madebased on fusion of the data from the sensors in the driver's smartwatch.See FIG. 4. Tailgating occurs when the driver is following too closebehind another vehicle which can be hazardous. Tailgating can happenwhen the driver is in a hurry and can be detected based on rapidacceleration and sudden braking as the driver closely follows thevehicle in front of them.

As provided above, the real-time data is collected from the driver'swearable mobile device (e.g., smartwatch) and analyzed in acontinuous/iterative manner. In the present example, the real-time dataread in step 402 from the accelerometer sensor indicates in step 404that the driver is making changes along the direction of travel. Forinstance, stepping on the brakes or accelerating makes changes in thevehicle's progression along the direction of travel. The question iswhether these changes occur repeatedly which can be indicative of thedriver following too closely behind another vehicle. For instance, arelatively constant speed can be maintained when following at a safedistance. However, when travelling too close to another vehicle,oftentimes constant braking followed by acceleration occurs. Of course,accelerations/decelerations occur during normal driving. Thus, to use asimple example, a threshold number of changes (along the direction oftravel) over a certain time period can be set (e.g., more than 2 changesin a 2 minute period) to determine whether there is a possibletailgating scenario.

Thus, in step 406, a determination is made as to whether the driver ismaking an excessive number of changes in direction of travel (e.g.,greater than the predetermined threshold number of changes in a givenduration). If it is determined in step 406 that (NO) the changes alongthe direction of travel are not excessive (e.g., they are consistentwith normal driving conditions), then the process continues to monitorthe driver data in real-time (see above). On the other hand, if it isdetermined in step 406 that (YES), the changes along the direction oftravel are excessive and thus could be indicative of a tailgatingsituation, then GPS data regarding the vehicle/drivers location isobtained in step 408, e.g., from the driver's smartwatch, and used instep 410 to determine the type of road the driver is on. GPS systems arecommonly equipped with road map capabilities which can be referenced todetermine what type of road (e.g., highway, small road, etc.) the driveris on. One reason for making a determination of road type is that ahighway has different driving conditions from a small local road forinstance. A small local road may have intersections and traffic lightsthat cause the driver to constantly stop and start, whereas relativelyconstant speeds are expected on a highway. Thus, a driver travellingthrough town might not be in fact tailgating when he/she has to brakeand accelerate on a regular basis. Thus having information about theroad type can be useful in assessing the driving conditions. It isnotable however that alerts can be generated (via the presenttechniques) whenever excessive changes along the direction of traveloccur.

Based on the road type determination, in step 412 a determination can bemade as to whether the driver is currently on a highway. If it isdetermined in step 412 that (NO) the driver is not presently on ahighway, then it may be assumed for example that the driver has torepeatedly make changes along the direction of travel, for example, inresponse to traffic signals, intersections, etc. The process thencontinues to monitor the driver data in real-time (see above).

On the other hand, if it is determined in step 412 that (YES) thevehicle is presently on a highway, then it may be inferred that theexcessive changes along the direction of travel might be due to thedriver tailgating another vehicle. Additionally, it may be useful toobtain physiological characteristics of the driver to make determinationof aggressive driving. For instance, when driving aggressively a drivermight have an elevated heart rate, anxiousness, etc. By further gaugingthe driver's physiological conditions, one can filter out scenarioswhere, e.g., the driver is on a highway and simply stuck in stop and gotraffic which causes the driver to repeatedly make changes along thedirection of travel.

Thus, according to an exemplary embodiment, in step 414 the driver'sheart rate is determined (based on data obtained, e.g., from thedriver's smartwatch), and a determination is made in step 416 as towhether the driver's heart rate is elevated. As provided above, theheart rate data can be evaluated based on past data collected from thedriver and/or based on standard heart rate data.

If it is determined in step 416 that (NO) the driver does not have anelevated heart rate, then it may be assumed, for example, that thedriver is simply stuck in stop and go traffic on a highway, and theprocess continues to monitor the driver data in real-time. On the otherhand, if it is determined in step 416 that (YES) the driver has anelevated heart rate, then it may be assumed that the driver is in anexcited state, potentially leading to aggressive driving behaviors suchas tailgating. In that case, it may be useful to use furtherphysiological data from the driver to confirm the situation. Again, thisis merely an example, and any of the above-described indicators ofaggressive driving may alone suffice to trigger an alert. By way ofexample only, the EDA data obtained, e.g., from the driver's smartwatch,can be used in step 418 to determine the driver's skin conductivity. Asindicated above, as an individual becomes more stressed their skinconductivity changes (due to sweat gland activity). Based on thedriver's skin conductivity data, in step 420 a determination is made asto whether the driver is stressed. For instance, typical baseline EDASCR readings for an individual without stress are from about 10 uS toabout 50 uS. Thus, any readings in excess of the baseline might indicatestress.

If it is determined in step 420 that (NO) the driver does not appearstressed, then the process continues to monitor the driver data inreal-time. On the other hand, if it is determined in step 420 that (YES)the driver appears stressed, then in step 422 a suggested action(feedback) is provided to the driver to warn the driver of the unsafecondition, such as “relax” and/or “stop following so closely.” Asprovided above, this feedback can be provided in a non-distractingmanner via an audio message from the driver's smartwatch and/or throughthe vehicle's audio system.

FIG. 5 illustrates an exemplary system 500 in which the presenttechniques can be implemented. As shown in FIG. 5, the driver's wearablemobile device (e.g., smartwatch 502)—worn while driving a vehicle504—collects real-time data from the driver as described above.According to one exemplary embodiment, the driver's smartwatch 502 isconfigured to take the data it collects, analyze it and, if appropriate,generate an alert as described above. In that case, the smartwatch isconfigured to perform the steps of methodology 100 of FIG. 1,methodology 200 of FIG. 2, methodology 300 of FIG. 3, and/or methodology400 of FIG. 4. Alternatively, as shown in FIG. 5, the real-time datacollected by the driver's smartwatch 502 can be transmitted (inreal-time) wirelessly to a central processing apparatus, such as server506. Server 506 may be configured to receive data from multiple driversvia their respective smartwatches. Smartwatches without a cell signal orWiFi connection would transmit via an associated smartphone (see FIG. 5)using short-range (100 m) communication such as Bluetooth Low Energy(BLE).

Server 506 may be located at a central processing facility. By way ofexample only, as provided above, the present techniques can beimplemented to provide feedback to insurance companies about the drivingsafety of its customers. In that case, the server 506 may belong to theinsurance company.

The real-time data collected via the driver's smartwatch can becollected by the server 506, analyzed in the manner described above and,if appropriate an alert can be sent to the driver in real-time. In thatscenario, server 506 is configured to perform the steps of methodology100 of FIG. 1, methodology 200 of FIG. 2, methodology 300 of FIG. 3,and/or methodology 400 of FIG. 4. An apparatus that can be configured toserve as server 506 is provided in FIG. 6, described below.

As described above, feedback may also be provided to the driver and/orother third parties (e.g., the parents of a driver) about his/herdriving record. That way, the driver can know what to improve on and/orwhat steps to take to drive more safely based on past experiences. Thus,as shown in FIG. 5, historical driving data can (optionally) be provided(e.g., by server 506) to one or more clients' personal computers 508.

The present invention may be a system, a method, and/or a computerprogram product. The computer program product may include a computerreadable storage medium (or media) having computer readable programinstructions thereon for causing a processor to carry out aspects of thepresent invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as Smalltalk, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Turning now to FIG. 6, a block diagram is shown of an apparatus 600 forimplementing one or more of the methodologies presented herein. Asdescribed in conjunction with the description of FIG. 5 above, apparatus600 may be configured to serve as server 506 in system 500. Thus,apparatus 600 can be configured to implement one or more of the steps ofmethodology 100 of FIG. 1, methodology 200 of FIG. 2, methodology 300 ofFIG. 3, and/or methodology 400 of FIG. 4.

Apparatus 600 includes a computer system 610 and removable media 650.Computer system 610 includes a processor device 620, a network interface625, a memory 630, a media interface 635 and an optional display 640.Network interface 625 allows computer system 610 to connect to anetwork, while media interface 635 allows computer system 610 tointeract with media, such as a hard drive or removable media 650.

Processor device 620 can be configured to implement the methods, steps,and functions disclosed herein. The memory 630 could be distributed orlocal and the processor device 620 could be distributed or singular. Thememory 630 could be implemented as an electrical, magnetic or opticalmemory, or any combination of these or other types of storage devices.Moreover, the term “memory” should be construed broadly enough toencompass any information able to be read from, or written to, anaddress in the addressable space accessed by processor device 620. Withthis definition, information on a network, accessible through networkinterface 625, is still within memory 630 because the processor device620 can retrieve the information from the network. It should be notedthat each distributed processor that makes up processor device 620generally contains its own addressable memory space. It should also benoted that some or all of computer system 610 can be incorporated intoan application-specific or general-use integrated circuit.

Optional display 640 is any type of display suitable for interactingwith a human user of apparatus 600. Generally, display 640 is a computermonitor or other similar display.

Although illustrative embodiments of the present invention have beendescribed herein, it is to be understood that the invention is notlimited to those precise embodiments, and that various other changes andmodifications may be made by one skilled in the art without departingfrom the scope of the invention.

What is claimed is:
 1. A method for alerting drivers of hazardousdriving conditions, the method comprising the steps of: collectingreal-time data from a driver of a vehicle, wherein the data is collectedvia a mobile device worn by the driver; determining whether thereal-time data indicates that a hazardous driving condition exists;providing feedback to the driver if the real-time data indicates thatthe hazardous driving condition exists; continuing to collect data fromthe driver in real-time if the real-time data indicates that thehazardous driving does not exist, wherein the method further comprisesthe steps of: detecting hand deviations of the driver using thereal-time data; determining whether the hand deviations of the driverinvolve side-to-side hand movements; estimating speed of the vehicleusing the real-time data if the hand deviations of the driver involveside-to-side hand movements; continuing to collect data from the driverin real-time if the hand deviations of the driver do not involveside-to-side hand movements; determining whether the speed of thevehicle is higher than an average speed for a roadway on which thevehicle is travelling; providing the feedback to the driver if the speedof the vehicle is higher than the average speed for the roadway on whichthe vehicle is travelling; and continuing to collect data from thedriver in real-time if the speed of the vehicle is not higher than anaverage speed for a roadway on which the vehicle is travelling.
 2. Themethod of claim 1, further comprising the step of: using the real-timedata to learn characteristics of the driver.
 3. The method of claim 2,further comprising the step of: comparing the real-time data to thecharacteristics of the driver to determine whether the real-time dataindicates that the hazardous driving condition exists.
 4. The method ofclaim 1, wherein the feedback is provided to the driver via the mobiledevice worn by the driver.
 5. The method of claim 4, wherein thefeedback is provided as an audible alert message to the driver via themobile device worn by the driver.
 6. The method of claim 1, wherein thefeedback is provided as an audible alert message to the driver via anaudio system of the vehicle.
 7. The method of claim 1, furthercomprising the step of: tracking feedback provided to the driver.
 8. Themethod of claim 1, wherein the real-time data collected from the usercomprises physiological data for the user selected from the groupconsisting of: skin electrical characteristics, blood oxygen levels,heart rate, pulse, and combinations thereof.
 9. The method of claim 1,wherein the real-time data collected from the user comprises trajectorydata for the user selected from the group consisting of: movement,speed, direction, orientation, location, and combinations thereof. 10.The method of claim 1, wherein the real-time data collected from theuser comprises environmental data selected from the group consisting of:air pressure, temperature, wind velocity, and combinations thereof. 11.The method of claim 1, wherein the mobile device worn by the usercomprises at least one sensor selected from the group consisting of: anelectrodermal activity (EDA) sensor, a pulse oximeter sensor, a heartrate sensor, and combinations thereof.
 12. The method of claim 1,wherein the mobile device worn by the user comprises at least one sensorselected from the group consisting of: a gyroscope sensor, a globalpositioning system (GPS) sensor, and combinations thereof.
 13. Themethod of claim 1, wherein the mobile device worn by the user comprisesat least one sensor selected from the group consisting of: a barometer,an air temperature sensor, a wind speed sensor, and combinationsthereof.
 14. The method of claim 1, wherein the mobile device worn bythe user comprises a smartwatch.